Deep Subspace Networks (DSN) [1]}: DSN predicts class membership by calculating the distance between the query point and its projections onto the latent subspaces formed by the support images for each class. This is analogous to our approach with \(r=1\) , with average-pooling performing the spatial reduction. The crucial difference is that DSN assumes (accurately) that \(d> k\) , whereas in our setting it is not always the case that \(d> kr\) . In fact, for many of our models \(S\)  spans the latent space, so the projection interpretation falls apart and we instead rely on the ridge regression regularizer to keep the problem well-posed.
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